如果你去看看the source code of this package,它只是用ggplot2
繪製在突圍檢測輸出的df
與事。
這裏是下面的代碼工作的例子
# your code
require(BreakoutDetection)
df.date = c("01-01-2017", "02-01-2017", "03-01-2017", "04-01-2017", "05-01-2017", "06-01-2017", "07-01-2017", "08-01-2017", "09-01-2017", "10-01-2017")
df.values = c(1,2,1,1,3,22,34,45,22, 10)
ts = data.frame(df.date, df.values)
ts.b = breakout(ts$df.values, min.size=3, method='multi', beta=.008, degree=1, plot=TRUE, xlab = "time")
ts.b$plot
# start:
library(ggplot2)
# the code in this function is copied from the source code of breakout detection (with small adjustment)
# you can adjust things
plot_breakout_detection = function(Z, retList, dateTime = T, x_lab = '', y_lab = '', title0 = ''){
if(class(Z)%in%c('numeric','integer') || ncol(Z) == 1){
dateTime = F
Z = data.frame(timestamp=1:length(Z), count = Z)
}
g = ggplot2::ggplot(Z, ggplot2::aes(x=timestamp, y=count)) + ggplot2::theme_bw() +
ggplot2::theme(panel.grid.minor=ggplot2::element_blank(), panel.grid.major=ggplot2::element_blank())
g = g + ggplot2::xlab(x_lab) + ggplot2::ylab(y_lab) + ggplot2::ggtitle(title0)
g = g + ggplot2::geom_line()
if(!is.null(retList$loc)&& length(retList$loc)>0){
v = retList$loc
v = c(0,v)
for(j in 2:length(v)){
M = mean(Z$count[(v[j-1]+1):v[j]])
df2 = data.frame(Z$timestamp[v[j]], Z$timestamp[v[j]], -Inf, M)
names(df2) = c('x','xend','y','yend')
g = g + ggplot2::geom_segment(data=df2,ggplot2::aes(x=x,y=y,xend=xend,yend=yend,color='2'),linetype=2,size=1.2)
g = g + ggplot2::guides(color=FALSE)
}
}
if(dateTime){
g = g + scale_x_datetime(expand=c(0,0)) + scale_y_continuous(expand=c(0,0))
} else {
g = g + scale_x_continuous(expand=c(0,0)) + scale_y_continuous(expand=c(0,0))
}
}
df = data.frame(timestamp = as.POSIXct(df.date, format = '%m-%d-%Y'), count = df.values)
g = plot_breakout_detection(df, ts.b)
g
嘗試提供[重複的例子(http://stackoverflow.com/questions/5963269/how-to-make-a-帶有示例輸入數據和代碼的偉大再現性示例),以便我們可以看到您正在嘗試執行的操作。 – MrFlick